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Record W4285797573 · doi:10.1017/s0144686x22000770

Age, ethnicity, life events and wellbeing among New Zealand women

2022· article· en· W4285797573 on OpenAlexaff
Nicky J. Newton, Chloe Howard, Carla Houkamau, Chris G. Sibley

Bibliographic record

VenueAgeing and Society · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsWilfrid Laurier University
FundersTempleton Religion Trust
KeywordsEthnic groupLife expectancyContext (archaeology)Life satisfactionMeaning (existential)DemographyIndigenousGerontologyPsychologyQuality of life (healthcare)PopulationGender studiesSociologyMedicineSocial psychologyGeography

Abstract

fetched live from OpenAlex

Abstract By the year 2030, 19–21 per cent of the population of New Zealand (NZ) is projected to be aged 65 and over. Like many countries, life expectancy in NZ differs by gender but also ethnicity: in 2019, life expectancy for Māori (indigenous) women was 77.1 years compared with 84.4 years for non-Māori women. If Māori and NZ European women are to flourish in later life, examining the factors associated with their wellbeing is paramount. The current study draws on the Life Course Perspective to explore how wellbeing is associated with age-related life events among mid- to later-life NZ women. The women in this study (N = 19,624) are participants in the 2018 wave of the New Zealand Attitudes and Values Study, a national probabilistic 20-year longitudinal study (mean age = 55.62; Māori = 10.8%, NZ European = 89.2%). We found that stressful life events were negatively associated with life satisfaction but positively associated with meaning in life. Māori women exhibited lower levels of life satisfaction but there were no ethnic differences for meaning in life; however, Māori and NZ European women showed different patterns of significant correlates associated with meaning in life. Findings highlight the necessity of an intersectional approach to the study of mid- to later-life wellbeing and the utility of measuring wellbeing in more than one way within NZ's unique cultural-historical context.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.294
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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